diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py b/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py
index 7a5ea0cb0f36c119755a9728f59582c9f798ab85..6f6b71eae0deda9176c3dcb32c76c99bccbf5f07 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py
@@ -69,14 +69,22 @@ class HpvmCodeGen(CodeGen):
# Variable indicator is always int for hpvm gen
variables: Dict[DFGNode, Tuple[int, bool]]
- def __init__(self, dfg: DFG, prefix: PathLike, input_size: int, target: str, inspectable: bool):
+ def __init__(
+ self,
+ dfg: DFG,
+ prefix: PathLike,
+ input_size: int,
+ target: str,
+ inspectable: Optional[dict],
+ ):
super().__init__(dfg, prefix, input_size)
if target not in ("tensor", "cudnn"):
raise ValueError(f"Unsupported target {target}")
self.target = target
self.template = template_env.get_template(
- INSPECT_TEMPLATE_FILE if inspectable else PLAIN_TEMPLATE_FILE
+ PLAIN_TEMPLATE_FILE if inspectable is None else INSPECT_TEMPLATE_FILE
)
+ self.inspect_vars = inspectable or {}
def _emit_hpvm_node_edges(self, input_vars: List[DFGNode]) -> List[dict]:
ret = []
@@ -147,5 +155,6 @@ class HpvmCodeGen(CodeGen):
weights=weights,
prefix=self.prefix,
target=self.target,
+ **self.inspect_vars
)
)
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
index 25b3fff2920320bab5f260b57991fc88563035d5..52b6a5bc21dbbe8df16af6972df370321013aca5 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
@@ -34,6 +34,8 @@ class ModelExporter:
weight_dir_name = "weights"
source_file_name = "hpvm_c.cpp"
metadata_file_name = "ops.json"
+ config_file_name = "tuner_confs.txt"
+ fifo_file_name = "hpvm_fifo"
def __init__(
self,
@@ -43,32 +45,43 @@ class ModelExporter:
output_dir: PathLike,
target: str = "hpvm_tensor",
opset: Optional[int] = None,
+ config_file: PathLike = None,
):
- from onnxsim import simplify
-
self.tune_dataset, self.test_dataset = tune_dataset, test_dataset
self.dataset_shape = self._check_datasets(tune_dataset, test_dataset)
self.dataset_size = self.dataset_shape[0]
- onnx_model = self._load_model(model, self.dataset_shape)
- if opset is not None:
- onnx_model = check_onnx_version(onnx_model, opset)
- onnx_model, check = simplify(onnx_model)
- assert check, "Simplified ONNX model could not be validated"
- onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
-
+ onnx_model = self._load_model(model, self.dataset_shape, opset)
self.dfg = DFG(onnx_model.graph)
- self.output_dir = Path(output_dir)
+
+ output_dir = Path(output_dir).absolute()
os.makedirs(output_dir, exist_ok=True)
- self.weight_dir = self.output_dir / self.weight_dir_name
+ self.weight_dir = output_dir / self.weight_dir_name
self.weight_dir.mkdir(exist_ok=True)
+ self.codefile = output_dir / self.source_file_name
+ self.metafile = output_dir / self.metadata_file_name
args3 = self.dfg, self.weight_dir, self.dataset_size
+ self.compile_args = None
if target == "hpvm_tensor":
- self.codegen = HpvmCodeGen(*args3, "tensor", False)
+ if config_file is None:
+ raise ValueError(
+ f"Config file must be given and exist under hpvm_tensor mode"
+ )
+ self.compile_args = ["-t", "tensor", "--conf-file", str(config_file)]
+ self.codegen = HpvmCodeGen(*args3, "tensor", None)
elif target == "hpvm_tensor_inspect":
- self.codegen = HpvmCodeGen(*args3, "tensor", True)
+ if config_file is None:
+ config_file = output_dir / self.config_file_name
+ else:
+ config_file = Path(config_file).absolute()
+ fifo_file = output_dir / self.fifo_file_name
+ self.compile_args = ["-t", "tensor", "--conf-file", str(config_file)]
+ inspect_mode_args = {"conf_path": config_file, "fifo_path": fifo_file}
+ self.codegen = HpvmCodeGen(*args3, "tensor", inspect_mode_args)
elif target == "hpvm_cudnn":
- self.codegen = HpvmCodeGen(*args3, "cudnn", False)
+ self.compile_target = "cudnn"
+ self.compile_args = ["-t", "cudnn"]
+ self.codegen = HpvmCodeGen(*args3, "cudnn", None)
elif target == "tensor":
self.codegen = TensorCodeGen(*args3)
else:
@@ -76,9 +89,11 @@ class ModelExporter:
def export_source_code(self, output: PathLike, batch_size: Optional[int] = None):
self.codegen.compile(output, batch_size)
+ return self
def export_weights(self):
self.dfg.dump_weights(self.weight_dir)
+ return self
def export_datasets(self):
input_, labels = self.tuneset_name
@@ -89,6 +104,7 @@ class ModelExporter:
self._dump_dataset(
self.test_dataset, self.weight_dir / input_, self.weight_dir / labels
)
+ return self
def export_metadata(
self, output: PathLike, approx_knobs_file: PathLike = def_approx_knobs_file
@@ -139,19 +155,38 @@ class ModelExporter:
"op_knobs": op_knobs,
"baseline_knob": baseline_knob,
"tune_args": "tune",
- "test_args": "test"
+ "test_args": "test",
},
f,
indent=2,
)
-
- def export_all(self, output: PathLike = None, batch_size: Optional[int] = None):
- default_codefile = self.output_dir / self.source_file_name
- self.export_source_code(output or default_codefile, batch_size)
- default_metafile = self.output_dir / self.metadata_file_name
- self.export_metadata(default_metafile)
+ return self
+
+ def compile(self, output_binary: PathLike, working_dir: Optional[PathLike] = None):
+ from subprocess import run
+
+ args = [
+ "approxhpvm.py",
+ str(self.codefile),
+ str(output_binary),
+ *self.compile_args,
+ ]
+ if working_dir is not None:
+ args.extend(["-d", str(working_dir)])
+ run(args, check=True)
+ return self
+
+ def generate(
+ self, output_code_file: PathLike = None, batch_size: Optional[int] = None
+ ):
+ self.codefile = (
+ self.codefile if output_code_file is None else Path(output_code_file)
+ )
+ self.export_source_code(self.codefile, batch_size)
+ self.export_metadata(self.metafile)
self.export_weights()
self.export_datasets()
+ return self
@staticmethod
def _dump_dataset(dataset: DatasetTy, input_filename: Path, labels_filename: Path):
@@ -229,7 +264,11 @@ class ModelExporter:
return dataset.shape
@staticmethod
- def _load_model(model: ModelTy, dataset_shape: Sequence[int]) -> onnx.ModelProto:
+ def _load_model(
+ model: ModelTy, dataset_shape: Sequence[int], opset: Optional[int]
+ ) -> onnx.ModelProto:
+ from onnxsim import simplify
+
if isinstance(model, Module):
# Export to ONNX and load back.
sample_input_shape = 1, *dataset_shape[1:]
@@ -237,10 +276,16 @@ class ModelExporter:
with NamedTemporaryFile("w+b") as tmp:
torch_to_onnx(model, (sample_input,), tmp)
tmp.seek(0)
- return onnx.load_model(tmp)
- if isinstance(model, onnx.ModelProto):
- return model
- return onnx.load(Path(model).as_posix())
+ onnx_model = onnx.load_model(tmp)
+ elif isinstance(model, onnx.ModelProto):
+ onnx_model = model
+ else:
+ raise ValueError(f"Cannot accept model of type {type(model)}")
+ if opset is not None:
+ onnx_model = check_onnx_version(onnx_model, opset)
+ onnx_model, check = simplify(onnx_model)
+ assert check, "Simplified ONNX model could not be validated"
+ return onnx.shape_inference.infer_shapes(onnx_model)
def check_onnx_version(model, new_version):
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
index 178a6d28e942fcd71c6ec18b6b4080866d06f1cb..00a15dc9553fcfab7ecbcda1be61e31cfde9dfd9 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
@@ -28,14 +28,19 @@ void fifo_write_finished(const std::string &filename) {
}
void make_fifo(const std::string &filename) {
- if (mkfifo(filename.c_str(), 0666) == 0)
+ if (mkfifo(filename.c_str(), 0666) == 0) {
+ std::ofstream file{filename};
+ file << "{{conf_path}}\n"; // Write path to config file in FIFO file
return;
+ }
+
if (errno == EEXIST) {
if (unlink(filename.c_str()) < 0) {
std::cout << "Error removing existing file: " << strerror(errno) << '\n';
abort();
}
make_fifo(filename);
+ return;
}
std::cout << "Error making FIFO file: " << strerror(errno) << '\n';
abort();
@@ -126,8 +131,8 @@ int main(int argc, char *argv[]){
args->{{n}}_bytes = 0;
{% endfor %}
- make_fifo("/tmp/hpvm_fifo");
- while (fifo_wait("/tmp/hpvm_fifo")) {
+ make_fifo("{{fifo_path}}");
+ while (fifo_wait("{{fifo_path}}")) {
__hpvm__init();
startMemTracking();
for (int i = 0; i < batch_count; i++){
@@ -143,7 +148,7 @@ int main(int argc, char *argv[]){
freeBatchMemory();
}
__hpvm__cleanup();
- fifo_write_finished("/tmp/hpvm_fifo");
+ fifo_write_finished("{{fifo_path}}");
}
return 0;
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
index 7395136eb5f19adc2ad3450c34b60c911f72747e..19f17366459a7684c6df8a940438b661cf7f6029 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
@@ -42,15 +42,11 @@ for model_cls, nch, img_size, batch_size, pathname in benchmarks:
checkpoint = self_folder / "../model_params" / f"{pathname}.pth.tar"
model.load_state_dict(torch.load(checkpoint.as_posix()))
- exporter = ModelExporter(model, bin_tuneset, bin_testset, codegen_dir)
- exporter.export_all(batch_size=batch_size)
-
- conf_file = self_folder / "../hpvm-c/benchmarks" / pathname / "data/tuner_confs.txt"
build_dir = codegen_dir / "build"
target_binary = build_dir / pathname
- run([
- "approxhpvm.py", str(codegen_dir / ModelExporter.source_file_name), str(target_binary),
- "-d", str(build_dir),
- "-t", "tensor", "--conf-file", str(conf_file)
- ], check=True)
+ conf_file = self_folder / "../hpvm-c/benchmarks" / pathname / "data/tuner_confs.txt"
+ exporter = ModelExporter(
+ model, bin_tuneset, bin_testset, codegen_dir, config_file=conf_file
+ )
+ exporter.generate(batch_size=batch_size).compile(target_binary, build_dir)
run([str(target_binary), "test"], check=True)
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py b/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
index 1c4e8120ef675a077757c593a0b295f31841a1ea..4da55559a2c2b5abe45d432aaac0930bada5faf5 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
@@ -53,18 +53,13 @@ model: Module = model_cls()
checkpoint = self_folder / "../model_params" / f"{pathname}.pth.tar"
model.load_state_dict(torch.load(checkpoint.as_posix()))
-exporter = ModelExporter(model, bin_tuneset, bin_testset, codegen_dir, target="hpvm_tensor_inspect")
-exporter.export_all(batch_size=batch_size)
-
-conf_file = self_folder / "../hpvm-c/benchmarks" / pathname / "data/tuner_confs.txt"
build_dir = codegen_dir / "build"
target_binary = build_dir / pathname
-run([
- "approxhpvm.py", str(codegen_dir / ModelExporter.source_file_name), str(target_binary),
- "-d", str(build_dir),
- "-t", "tensor", "--conf-file", str(conf_file)
-], check=True)
-# run([str(target_binary), "test"], check=True)
+exporter = ModelExporter(
+ model, bin_tuneset, bin_testset, codegen_dir, target="hpvm_tensor_inspect"
+)
+exporter.generate(batch_size=batch_size).compile(target_binary, build_dir)
+run([str(target_binary), "test"], check=True)
# build_dir = codegen_dir / "build"
# print(PipedBinaryApp("test", codegen_dir / "ops.json", build_dir / "lenet_mnist", build_dir))